Missing diversity, equity, and inclusion elements in affective computing datasets directly affect the accuracy and fairness of emotion recognition algorithms across different groups. A literature review reveals how affective computing systems may work differently for different groups due to, for instance, mental health conditions impacting facial expressions and speech or age-related changes in facial appearance and health. Our work analyzes existing affective computing datasets and highlights a disconcerting lack of diversity in current affective computing datasets regarding race, sex/gender, age, and (mental) health representation. By emphasizing the need for more inclusive sampling strategies and standardized documentation of demographic factors in datasets, this paper provides recommendations and calls for greater attention to inclusivity and consideration of societal consequences in affective computing research to promote ethical and accurate outcomes in this emerging field.

In healthcare, gender and sex considerations are crucial because they affect individuals' health and disease differences. Yet, most algorithms deployed in the healthcare context do not consider these aspects and do not account for bias detection. Missing these dimensions in algorithms used in medicine is a huge point of concern, as neglecting these aspects will inevitably produce far from optimal results and generate errors that may lead to misdiagnosis and potential discrimination. This paper explores how current algorithmic-based systems may reinforce gender biases and affect marginalized communities in healthcare-related applications. To do so, we bring together notions and reflections from computer science, queer media studies, and legal insights to better understand the magnitude of failing to consider gender and sex difference in the use of algorithms for medical purposes. Our goal is to illustrate the potential impact that algorithmic bias may have on inadvertent discriminatory, safety, and privacy-related concerns for patients in increasingly automated medicine. This is necessary because by rushing the deployment of AI technologies that do not account for diversity, we risk having an even more unsafe and inadequate healthcare delivery. By promoting the account for privacy, safety, diversity, and inclusion in algorithmic developments with health-related outcomes, we ultimately aim to inform the Artificial Intelligence (AI) global governance landscape and practice on the importance of integrating gender and sex considerations in the development of algorithms to avoid exacerbating existing or new prejudices.

Recent advancements in socially assistive robotics (SAR) have shown a significant potential of using social robotics to achieve increasing cognitive and affective outcomes in education. However, the deployments of SAR technologies also bring ethical challenges in tandem, to the fore, especially in under-resourced contexts. While previous research has highlighted various ethical challenges that arise in SAR deployment in real-world settings, most of the research has been centered in resource-rich contexts, mainly in developed countries in the ‘Global North,’ and the work specifically in the educational setting is limited. This research aims to evaluate and reflect upon the potential ethical and pedagogical challenges of deploying a social robot in an under-resourced context. We base our findings on a 5-week in-the-wild user study conducted with 12 kindergarten students at an under-resourced community school in New Delhi, India. We used interaction analysis with the context of learning, education, and ethics to analyze the user study through video recordings. Our findings highlighted four primary ethical considerations that should be taken into account while deploying social robotics technologies in educational settings; (1) language and accent as barriers in pedagogy, (2) effect of malfunctioning, (un)intended harms, (3) trust and deception, and (4) ecological viability of innovation. Overall, our paper argues for assessing the ethical and pedagogical constraints and bridging the gap between non-existent literature from such a context to evaluate better the potential use of such technologies in under-resourced contexts.

Social media platforms employ inferential analytics methods to guess user preferences and may include sensitive attributes such as race, gender, sexual orientation, and political opinions. These methods are often opaque, but they can have significant effects such as predicting behaviors for marketing purposes, influencing behavior for profit, serving attention economics, and reinforcing existing biases such as gender stereotyping. Although two international human rights treaties include express obligations relating to harmful and wrongful stereotyping, these stereotypes persist both online and offline, and platforms often appear to fail to understand that gender is not merely a binary of being a 'man' or a 'woman,' but is socially constructed. Our study investigates the impact of algorithmic bias on inadvertent privacy violations and the reinforcement of social prejudices of gender and sexuality through a multidisciplinary perspective including legal, computer science, and queer media viewpoints. We conducted an online survey to understand whether and how Twitter inferred the gender of users. Beyond Twitter's binary understanding of gender and the inevitability of the gender inference as part of Twitter's personalization trade-off, the results show that Twitter misgendered users in nearly 20% of the cases (N=109). Although not apparently correlated, only 8% of the straight male respondents were misgendered, compared to 25% of gay men and 16% of straight women. Our contribution shows how the lack of attention to gender in gender classifiers exacerbates existing biases and affects marginalized communities. With our paper, we hope to promote the online account for privacy, diversity, and inclusion and advocate for the freedom of identity that everyone should have online and offline. Also available on ResearchGate.